{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984772881e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print 'Testing affine_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  2.51581908054e-10\n",
      "dw error:  5.54912138208e-11\n",
      "db error:  5.23004149753e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print 'Testing affine_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-8\n",
    "print 'Testing relu_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27561948298e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-12\n",
    "print 'Testing relu_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  8.30154859019e-11\n",
      "dw error:  1.34576892332e-10\n",
      "db error:  1.03124806374e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print 'Testing affine_relu_forward:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.99977646138\n",
      "dx error:  8.18289447289e-10\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.30256318505\n",
      "dx error:  1.02471999002e-08\n"
     ]
    }
   ],
   "source": [
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print 'Testing svm_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print '\\nTesting softmax_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.57e-10\n",
      "b1 relative error: 8.37e-09\n",
      "b2 relative error: 2.53e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.09e-09\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-2\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print 'Testing initialization ... '\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print 'Testing test-time forward pass ... '\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print 'Testing training loss (no regularization)'\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print 'Running numeric gradient check with reg = ', reg\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 392) loss: 2.456508\n",
      "(Epoch 0 / 2) train acc: 0.208000; val_acc: 0.212000\n",
      "(Iteration 101 / 392) loss: 2.039444\n",
      "(Epoch 1 / 2) train acc: 0.387000; val_acc: 0.392000\n",
      "(Iteration 201 / 392) loss: 1.969064\n",
      "(Iteration 301 / 392) loss: 1.828885\n",
      "(Epoch 2 / 2) train acc: 0.425000; val_acc: 0.404000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAALXCAYAAAAqrWlxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd50e3b6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = TwoLayerNet(hidden_dim = 200,reg = 0.5)\n",
    "solver = Solver(model, data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=2, batch_size=250,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAALXCAYAAAAqrWlxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd55825a9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30199854936\n",
      "W1 relative error: 1.25e-06\n",
      "W2 relative error: 1.68e-07\n",
      "W3 relative error: 2.79e-07\n",
      "b1 relative error: 1.19e-08\n",
      "b2 relative error: 2.33e-09\n",
      "b3 relative error: 1.03e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.91641009364\n",
      "W1 relative error: 3.87e-08\n",
      "W2 relative error: 2.87e-08\n",
      "W3 relative error: 2.56e-07\n",
      "b1 relative error: 1.90e-08\n",
      "b2 relative error: 4.94e-09\n",
      "b3 relative error: 8.39e-11\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print 'Running check with reg = ', reg\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print 'Initial loss: ', loss\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 45.260120\n",
      "(Epoch 0 / 20) train acc: 0.280000; val_acc: 0.108000\n",
      "(Epoch 1 / 20) train acc: 0.260000; val_acc: 0.116000\n",
      "(Epoch 2 / 20) train acc: 0.400000; val_acc: 0.092000\n",
      "(Epoch 3 / 20) train acc: 0.560000; val_acc: 0.120000\n",
      "(Epoch 4 / 20) train acc: 0.620000; val_acc: 0.096000\n",
      "(Epoch 5 / 20) train acc: 0.860000; val_acc: 0.131000\n",
      "(Iteration 11 / 40) loss: 0.526866\n",
      "(Epoch 6 / 20) train acc: 0.920000; val_acc: 0.140000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.137000\n",
      "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.137000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.136000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.122000\n",
      "(Iteration 21 / 40) loss: 0.004101\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.129000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Iteration 31 / 40) loss: 0.001879\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.128000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.130000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAH4CAYAAADttlFjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQAMJoABAAwmgAEADPb/AzIVpj2ZfgpdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5101b0210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.55860209465\n"
     ]
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "import time\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "\n",
    "# to find to best hyper params\n",
    "\n",
    "# for x in range(10):\n",
    "#     weight_scale = 10**np.random.uniform(-2,-1)\n",
    "#     learning_rate = 10**np.random.uniform(-4,-3)\n",
    "\n",
    "#     print weight_scale, learning_rate\n",
    "#     model = FullyConnectedNet([100, 100],\n",
    "#                   weight_scale=weight_scale, dtype=np.float64)\n",
    "#     solver = Solver(model, small_data,\n",
    "#                     print_every=10, num_epochs=20, batch_size=25,\n",
    "#                     update_rule='sgd',\n",
    "#                     optim_config={\n",
    "#                       'learning_rate': learning_rate,\n",
    "#                     }\n",
    "#              )\n",
    "#     solver.train()\n",
    "\n",
    "#     plt.plot(solver.loss_history, 'o')\n",
    "#     plt.title('Training loss history')\n",
    "#     plt.xlabel('Iteration')\n",
    "#     plt.ylabel('Training loss')\n",
    "#     plt.show()\n",
    "    \n",
    "weight_scale = 0.06\n",
    "learning_rate = 0.0004\n",
    "\n",
    "t1 = time.time()\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()\n",
    "t2 = time.time()\n",
    "print t2 - t1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 13.446128\n",
      "(Epoch 0 / 20) train acc: 0.140000; val_acc: 0.104000\n",
      "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.097000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.111000\n",
      "(Epoch 3 / 20) train acc: 0.340000; val_acc: 0.102000\n",
      "(Epoch 4 / 20) train acc: 0.400000; val_acc: 0.112000\n",
      "(Epoch 5 / 20) train acc: 0.560000; val_acc: 0.115000\n",
      "(Iteration 11 / 40) loss: 3.584044\n",
      "(Epoch 6 / 20) train acc: 0.540000; val_acc: 0.105000\n",
      "(Epoch 7 / 20) train acc: 0.600000; val_acc: 0.101000\n",
      "(Epoch 8 / 20) train acc: 0.640000; val_acc: 0.102000\n",
      "(Epoch 9 / 20) train acc: 0.760000; val_acc: 0.110000\n",
      "(Epoch 10 / 20) train acc: 0.780000; val_acc: 0.108000\n",
      "(Iteration 21 / 40) loss: 0.604448\n",
      "(Epoch 11 / 20) train acc: 0.880000; val_acc: 0.105000\n",
      "(Epoch 12 / 20) train acc: 0.840000; val_acc: 0.105000\n",
      "(Epoch 13 / 20) train acc: 0.920000; val_acc: 0.107000\n",
      "(Epoch 14 / 20) train acc: 0.940000; val_acc: 0.110000\n",
      "(Epoch 15 / 20) train acc: 0.960000; val_acc: 0.109000\n",
      "(Iteration 31 / 40) loss: 0.321079\n",
      "(Epoch 16 / 20) train acc: 0.960000; val_acc: 0.109000\n",
      "(Epoch 17 / 20) train acc: 0.980000; val_acc: 0.105000\n",
      "(Epoch 18 / 20) train acc: 0.960000; val_acc: 0.112000\n",
      "(Epoch 19 / 20) train acc: 0.980000; val_acc: 0.106000\n",
      "(Epoch 20 / 20) train acc: 0.960000; val_acc: 0.109000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAH4CAYAAADttlFjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAEAdCaAAQB0JoABAHQmgAEAdPb/A8oOnU/y/QGyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5101b0410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.69545102119\n"
     ]
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 0.07\n",
    "learning_rate = 0.0004\n",
    "\n",
    "t1 = time.time()\n",
    "\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()\n",
    "t2 = time.time()\n",
    "print t2 - t1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "Maybe more computational expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(next_w, expected_next_w)\n",
    "print 'velocity error: ', rel_error(expected_velocity, config['velocity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 3.367396\n",
      "(Epoch 0 / 5) train acc: 0.071000; val_acc: 0.089000\n",
      "(Iteration 11 / 200) loss: 2.208602\n",
      "(Iteration 21 / 200) loss: 2.004928\n",
      "(Iteration 31 / 200) loss: 2.135670\n",
      "(Epoch 1 / 5) train acc: 0.297000; val_acc: 0.267000\n",
      "(Iteration 41 / 200) loss: 2.060847\n",
      "(Iteration 51 / 200) loss: 1.995528\n",
      "(Iteration 61 / 200) loss: 1.939779\n",
      "(Iteration 71 / 200) loss: 1.858604\n",
      "(Epoch 2 / 5) train acc: 0.351000; val_acc: 0.294000\n",
      "(Iteration 81 / 200) loss: 1.963035\n",
      "(Iteration 91 / 200) loss: 1.771007\n",
      "(Iteration 101 / 200) loss: 1.803743\n",
      "(Iteration 111 / 200) loss: 1.874625\n",
      "(Epoch 3 / 5) train acc: 0.381000; val_acc: 0.318000\n",
      "(Iteration 121 / 200) loss: 1.734668\n",
      "(Iteration 131 / 200) loss: 1.855827\n",
      "(Iteration 141 / 200) loss: 1.827266\n",
      "(Iteration 151 / 200) loss: 1.704259\n",
      "(Epoch 4 / 5) train acc: 0.400000; val_acc: 0.311000\n",
      "(Iteration 161 / 200) loss: 1.723074\n",
      "(Iteration 171 / 200) loss: 1.877850\n",
      "(Iteration 181 / 200) loss: 1.570044\n",
      "(Iteration 191 / 200) loss: 1.701101\n",
      "(Epoch 5 / 5) train acc: 0.428000; val_acc: 0.324000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.651126\n",
      "(Epoch 0 / 5) train acc: 0.102000; val_acc: 0.108000\n",
      "(Iteration 11 / 200) loss: 2.257808\n",
      "(Iteration 21 / 200) loss: 2.077007\n",
      "(Iteration 31 / 200) loss: 1.977166\n",
      "(Epoch 1 / 5) train acc: 0.338000; val_acc: 0.293000\n",
      "(Iteration 41 / 200) loss: 2.014687\n",
      "(Iteration 51 / 200) loss: 1.800317\n",
      "(Iteration 61 / 200) loss: 1.782147\n",
      "(Iteration 71 / 200) loss: 1.789136\n",
      "(Epoch 2 / 5) train acc: 0.383000; val_acc: 0.343000\n",
      "(Iteration 81 / 200) loss: 1.849358\n",
      "(Iteration 91 / 200) loss: 1.656330\n",
      "(Iteration 101 / 200) loss: 1.557266\n",
      "(Iteration 111 / 200) loss: 1.730674\n",
      "(Epoch 3 / 5) train acc: 0.456000; val_acc: 0.348000\n",
      "(Iteration 121 / 200) loss: 1.585649\n",
      "(Iteration 131 / 200) loss: 1.500091\n",
      "(Iteration 141 / 200) loss: 1.366878\n",
      "(Iteration 151 / 200) loss: 1.321692\n",
      "(Epoch 4 / 5) train acc: 0.479000; val_acc: 0.320000\n",
      "(Iteration 161 / 200) loss: 1.475079\n",
      "(Iteration 171 / 200) loss: 1.434218\n",
      "(Iteration 181 / 200) loss: 1.384923\n",
      "(Iteration 191 / 200) loss: 1.484816\n",
      "(Epoch 5 / 5) train acc: 0.491000; val_acc: 0.354000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAAN/CAYAAAB5lfFGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nC4NsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd50e55a610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print 'running with ', update_rule\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'cache error: ', rel_error(expected_cache, config['cache'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.13988746733e-07\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'v error: ', rel_error(expected_v, config['v'])\n",
    "print 'm error: ', rel_error(expected_m, config['m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.751725\n",
      "(Epoch 0 / 5) train acc: 0.146000; val_acc: 0.120000\n",
      "(Iteration 11 / 200) loss: 2.083747\n",
      "(Iteration 21 / 200) loss: 2.087792\n",
      "(Iteration 31 / 200) loss: 1.971287\n",
      "(Epoch 1 / 5) train acc: 0.363000; val_acc: 0.303000\n",
      "(Iteration 41 / 200) loss: 1.716005\n",
      "(Iteration 51 / 200) loss: 1.794610\n",
      "(Iteration 61 / 200) loss: 1.404052\n",
      "(Iteration 71 / 200) loss: 1.728169\n",
      "(Epoch 2 / 5) train acc: 0.422000; val_acc: 0.341000\n",
      "(Iteration 81 / 200) loss: 1.539902\n",
      "(Iteration 91 / 200) loss: 1.547422\n",
      "(Iteration 101 / 200) loss: 1.523125\n",
      "(Iteration 111 / 200) loss: 1.409608\n",
      "(Epoch 3 / 5) train acc: 0.492000; val_acc: 0.365000\n",
      "(Iteration 121 / 200) loss: 1.351967\n",
      "(Iteration 131 / 200) loss: 1.447324\n",
      "(Iteration 141 / 200) loss: 1.339464\n",
      "(Iteration 151 / 200) loss: 1.218762\n",
      "(Epoch 4 / 5) train acc: 0.542000; val_acc: 0.355000\n",
      "(Iteration 161 / 200) loss: 1.332783\n",
      "(Iteration 171 / 200) loss: 1.358003\n",
      "(Iteration 181 / 200) loss: 1.252637\n",
      "(Iteration 191 / 200) loss: 1.117633\n",
      "(Epoch 5 / 5) train acc: 0.590000; val_acc: 0.390000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.681561\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.104000\n",
      "(Iteration 11 / 200) loss: 2.054902\n",
      "(Iteration 21 / 200) loss: 1.957956\n",
      "(Iteration 31 / 200) loss: 1.903801\n",
      "(Epoch 1 / 5) train acc: 0.351000; val_acc: 0.289000\n",
      "(Iteration 41 / 200) loss: 1.851657\n",
      "(Iteration 51 / 200) loss: 1.708149\n",
      "(Iteration 61 / 200) loss: 1.680305\n",
      "(Iteration 71 / 200) loss: 1.693913\n",
      "(Epoch 2 / 5) train acc: 0.431000; val_acc: 0.339000\n",
      "(Iteration 81 / 200) loss: 1.528903\n",
      "(Iteration 91 / 200) loss: 1.689201\n",
      "(Iteration 101 / 200) loss: 1.593781\n",
      "(Iteration 111 / 200) loss: 1.480716\n",
      "(Epoch 3 / 5) train acc: 0.502000; val_acc: 0.343000\n",
      "(Iteration 121 / 200) loss: 1.580321\n",
      "(Iteration 131 / 200) loss: 1.544179\n",
      "(Iteration 141 / 200) loss: 1.591640\n",
      "(Iteration 151 / 200) loss: 1.359766\n",
      "(Epoch 4 / 5) train acc: 0.483000; val_acc: 0.348000\n",
      "(Iteration 161 / 200) loss: 1.401508\n",
      "(Iteration 171 / 200) loss: 1.446586\n",
      "(Iteration 181 / 200) loss: 1.319031\n",
      "(Iteration 191 / 200) loss: 1.262970\n",
      "(Epoch 5 / 5) train acc: 0.539000; val_acc: 0.349000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAAN/CAYAAAB5lfFGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd50e7481d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print 'running with ', update_rule\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(X_test), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(X_val), axis=1)\n",
    "print 'Validation set accuracy: ', (y_val_pred == y_val).mean()\n",
    "print 'Test set accuracy: ', (y_test_pred == y_test).mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
